1. Field of the Invention
The present invention relates generally to a system for creating and editing expert rules to be used in an expert system and a method of using the system.
2. Discussion of the Related Art
The healthcare industry uses information technology to track many different parameters pertaining to different aspects of patient care. For instance, in a hospital setting, patient demographic information is collected and stored when a patient first checks into a hospital for care. The hospital personnel then have access to a variety of information regarding the patient, such as health insurance provider, primary physician and previous health history. Additionally, the database may contain information from previous visits or stays at that hospital. If the patient has checked into the hospital previously or used the services of a hospital subsidiary, any previous test result information may be available to hospital personnel as well. This data represents a wealth of information regarding, for example, the types and frequency of infectious diseases in the community serviced by the hospital.
Most hospitals maintain infectious disease departments. The infectious disease department tracks the types of infectious diseases that have entered the hospital via patients and also those infectious diseases that still remain in the hospital. For instance, during the recent SARS outbreak in Hong Kong, patient demographic information was of interest because it could help localize the area where the infections occurred or limit the infections to a particular demographic of the community, such as cruise line employees. By storing information regarding patient demographics and the types of infectious diseases, a database is generated from which data can be extracted to determine different features of the infectious disease. As another example, analysis of the patient demographic data may pinpoint that an infectious bacterium only infects the elderly during the summer months.
Independent laboratories also maintain databases of patient demographic information and the results of all of the various tests performed at each laboratory. These labs look for trends and patterns within their databases so they can provide greater service to the physicians that use their services. Whether a physician uses a hospital laboratory or an independent laboratory, the ability of these laboratories to analyze the data each has collected and stored in a database provides physicians with valuable information regarding the treatment of an infection.
Referring to FIG. 1, the clinical database 10 receives test results from a variety of test sources that provide different perspectives of a given organism. For instance, if an infectious disease is tested in an identification (ID) and antimicrobial susceptibility test (AST) 60, the physician will be provided with data regarding the probable identity of the bacterium as well as the antibiotics that may destroy the bacterium. In addition, the physician will be provided with information about the doses necessary to kill the bacterium.
Other tests 50 use deoxyribonucleic acid (DNA) methods to detect sexually transmitted diseases (STDs), such as gonorrhea and Chlamydia, and the results are stored in the database 10. The results from blood culture test 20, as well as other tests 30, are also stored in the database 10.
The clinical database 10 stores the clinical test result data. The clinical database 10 has patient demographic information 40 similar to the hospital check-in database. Over the past decades, the hospital databases contain more and more information regarding a wide range of infectious disease, as well as patient demographic information. Additionally, the tests, 20, 50 and 60 may be capable of receiving information from the clinical database 10.
Microbiologists use clinical databases 10 to monitor the evolution of bacteria, viruses and other microorganisms. Today, the field of microbiology is a complicated mix of evolving microorganisms, drugs, and information. Expert systems were developed to take advantage of the extensive amount of information accumulated regarding the interaction of drugs, human subjects and microorganisms. The expert system is able to identify patterns of interactions between drugs, human subjects and microorganisms and provide a microbiologist with a probable result based on these patterns of interactions. But as new drugs are developed, as microorganisms develop resistance to drugs, the microbiologist must also change the expert system to react to these developments and changes.
In order to analyze the data, expert systems were developed to perform analysis of the data stored in the various clinical and hospital databases. The expert system typically is a rules-based system that analyzes data to prove a hypothesis regarding the data under test. Rules are written so a user may check the clinical database for information regarding drug result patterns, patient demographic patterns, specimen information and other related information stored on clinical database. Rules comprise a set of conditions and a set of actions to perform when the conditions are met. The rules are typically in the form of a question with an IF-THEN format. The hypothesis to prove is the basis for which types of questions to ask. For example, to prove that species Escherichia coli are resistant to a form of penicillin, such as, Ampicillin, the question may be IF Ampicillin does not kill this species of Escherichia coli THEN this species of Escherichia coli is resistant to Ampicillin. This would be an example of the high-level logic from which a syntax and structure intensive expert rule would be formed.
As stated above, the creation of a rule for a conventional expert system is time consuming because several people are involved and each must perform a separate task. Referring to FIG. 2, typically, a microbiologist conceives a concept for a rule (S200), but must wait to discuss the rule concept with a microspecialist, who is familiar with the expert system. After discussing the rule concept with the microbiologist, the microspecialist formulates a logical expression of the conceptual rule (S220). Finally, a software engineer places the logical expression into the proper structure and syntax for execution by the expert system (S240). Once this is done, the microbiologist, microspecialist and software engineer (collectively, the developers) await the result output by the expert system (S260). If the rule successfully runs or executes on the expert system, the developers have done their jobs. The rule can be applied to data from the clinical database, potentially modifying that data (S280). However, one task remains to be done. The microbiologist must now determine if the rule is providing the expected or a satisfactory output. To do this, the microbiologist will have to input different data sets of either made-up data or real data mined from the database. This, too, can be a time consuming task and cannot be done automatically by the expert system.
If the rule fails in step S260, the software engineer must check his or her work, the microspecialist must check his or her work, and the microbiologist must wait to perform his or her review of the results. Therefore, when the rules input by the users do not follow the syntax, the expert system will not interpret the rule and may not even inform the users of the cause of the syntax error. This frustrates the users of the expert system. Even more frustrating are minor logic or syntax errors that are interpreted by the system, but do not generate the result expected by the user. To avoid frustrating the users, the vendors of the expert systems must provide a considerable amount of training to teach the users the correct syntax and rule structure. The time constraints on both the vendor and the user typically cause training to be brief or incomplete. Another disadvantage to extensive training is not only the expense of providing the training, but the actual time lost when the users could be performing other tasks.
Finally, in order for the expert system to be widely accepted by users both in the United States and abroad, the expert system must accommodate a multitude of standards set by both governmental and non-governmental organizations. For instance, some of the organizations that provide such standards are the German Standards Institute (Deutsches Institut für Normung or DIN), and the National Committee for Clinical Laboratory Standards (NCCLS). If the expert system does not meet the standards to which the user hospital or independent laboratory certifications are held, these users will not purchase the expert system from the vendor.
Therefore, there is a need for an expert system that allows for easy rule creation, while accommodating a large percentage of the standards set by the relevant governmental and non-governmental organizations.